Broadly, we quantify curviness by clustering \(X^\text{d}\)  cells into interstripes in Step 4 and then tracing out the upper and lower boundaries of each interstripe in Step 5. We use the method introduced in [1]} to specify the number of interstripe features (i.e., the target number of clusters \(N_\text{cl}\) ) and apply single-linkage hierarchical clustering to the positions of \(X^\text{d}\)  cells to produce \(N_\text{cl}\)  clusters. In the ABM [2]}, these clusters generally correspond directly to interstripes. Because of differences in the noise and messiness present in ABM [2]} and CA [4]} patterns, however, we find that the same process does not transfer to the on-lattice model [4]}. Instead, as in the CA pattern in Figure REF (h), gold spots or stray \(X^\text{d}\)  cells that remain after our initial cleaning in Step 2 can disrupt clustering. To account for these challenges, we introduce an iterative process to detect and remove clusters of \(X^\text{d}\)  cells that do not correspond to interstripes. This is a second round of data cleaning, removing more cells beyond those that we already removed in Step 2.
